ActiLife 6 is ActiGraph's premier actigraphy data analysis and management platform. Trusted by researchers and healthcare providers around the world, ActiLife 6 is used to prepare ActiGraph devices for data collection and to download, process, score and securely manage collected data. ActiLife 6 has an extensive selection of integrated customer-driven features and analysis tools designed to help our clients achieve a broad range of research and clinical objectives.
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ActiLife will produce the same cut point categorization result regardless of epoch length beginning with version 6.8. With versions 6.7.1 and older, ActiLife categorized epoch values into cut point "buckets" by scaling the count level up to its 60s equivalent. The example shown below summarizes the difference in cut point results for a 10s epoch file and its 60s equivalent over a period of 5 minutes. Note the 10s breakdown of cut point results for the smaller epoch file.

OLD WAY (ActiLife 6.7.1 and older [before August 2013])

With ActiLife 6.8 and newer, the results will be identical, regardless of the file's epoch length.

NEW WAY (ActiLife 6.8.0 and newer [After August 2013])

The figure below shows the differences in how ActiLife will calculate results

Why did we change?

After speaking with several industry leaders, we determined that the cut points themselves were not designed to be compared to scaled, sub-60s epoch count levels. Documentation outlined in the help article "What's the difference among the Cut Points available in ActiLife?" further validates this decision. All of the cut points supported in ActiLife were designed to be used only with 60s files.

What is the impact on my data?If you have processed cut points in the past using sub-60s epoch files, your results may not be comparable. Those files can be reprocessed in the new version of ActiLife quickly since we always ensure backward compatibility. Your original epoch and raw data is never changed by ActiLife so you can always reproduce results at anytime.

We do our best to avoid changing algorithm results but we also want to inform our users if we're forced to do so. We appreciate your understanding; please feel free to contact us with any questions.

If the data appears shifted or jumbled up within ActiLife, changing the DPI Settings to 96 DPI or normal fonts will correct the problem you are experiencing.

To change the setting within Windows XP:

On your desktop, right click and select Properties

A box will appear called Display Properties. Click on the Settingstab.

3. Click on Advanced

4. Change the DPI setting to Normal Size (96 DPI). If you have the value set to anything else, text and images on in ActiLife will look jumbled on the screen.Click OK and your computer will ask you to reboot your computer. Click Yes.

To change the setting on Windows Vista\7:1. On your desktop, right click and select "Personalize"2. In the new window, towards to bottom left corner, click on the Display link.3. You will now get a new screen4. Select Smaller - 100% (default) and click Apply.5. Click on Log Off button to apply the DPI changes. Log back on to see the changes.

There are two flavours from the ActiLife software, the full and the lite. A full version means that you can do everything with the software that's possible like initialization, import patient information, import data from device to software and analyzing data. The lite version looks exactly the same, but the big difference is that with this version it's only possible to export data and initialize the device again. This saves time and is expecially helpfull in big research projects with more then one researcher involved. Analyzing is not possible with the lite version, the full version has to be used in that case!

As a measure of security and controlling what external resources can be accessed from an Organizations network, some IT Departments may have implemented Proxy Servers. These Servers direct all internet/network traffic through a specific computer which determines, based on the configuration set up by the Administrators, whether that website is accessible to a machine located on that network. This is done to prevent users from accessing websites that could introduce security concerns like viruses or malware, or that are simply deemed inappropriate for use on that network (i.e. Facebook, Netflix, Youtube, game-related sites, etc).

In order to check for program updates, to use the Data Vault, and to verify subscription status (for ActiLife 6.x users), ActiLife does require access via Port 80 (normal web traffic) and Port 443 (secure web traffic). In some instances, these Proxy Servers may block or otherwise obstruct this communication. If you encounter issues activating, opening, or using the features within ActiLife, please first verify with your IT department whether or not they have implemented a Proxy Server and if so, what the address of the Proxy server is and what port all traffic should be redirected to use.

Beginning with ActiLife 6.3, we have added basic Proxy Support. In order to properly configure this, you will need to know what the Proxy server's IP address is, the Port number, and any username or password that may be required. The default settings (i.e. Auto-Detect) might be all that is required but depending on how things are set up, you may need to manually enter this information. You can get to these settings by opening ActiLife and going to Edit, then Options, and selecting Proxy on the left side of the screen. Complete the box and click OK. (Please refer to the following image:)

As of ActiLife version 6.1.x, there are nine sets of Cut Points available to users. These cut points were derived as part of past published research aimed at quantifying activity levels using ActiGraph products. Definitions of the cut point levels and references to the corresponding published research are given below. Note that the published research is only referenced by its abstract; full publications are available for purchase from the corresponding journal. Note that all cut point sets are scaled to 60s epochs. Even if the cut point set was originally defined for sub-60s epoch files, the cut points were scaled in ActiLife in CPM; Counts Per Minute.

Though these cut points were derived with legacy versions of our ActiGraph hardware, they still apply today because of backward compatibility of ActiGraph products. The cut points were derived based on the MET thresholds described in the reference research. The Lifestyle cut point was added after a verbal discussion with the author regarding the need for an intermediary cut point level. Contact the author for further explanation.

Fifty participants wore the GT3X and the GT1M on the non-dominant hip and exercised at 4 treadmill speeds (4.8, 6.4, 9.7, and 12 km h?1). Vertical (VT) and antero-posterior (AP) activity counts (counts min?1) as well as the vector magnitudes of the two axes (VM2) from both monitors were tested for significant differences using two-way ANOVA’s. Bland–Altman plots were used to assess agreement between activity counts from the GT3X and GT1M. Linear regression analysis between VM3 counts min?1 and oxygen consumption data was conducted to develop VM3 cut-points for moderate, hard and very hard PA.

Devices were worn on the hip or lower leg, were validated and calibrated against 6-hour EE measurements by room respiration calorimetry, activity by microwave detector, and heart rate by telemetry in 26 children. 6 to 16 years old. During the 6 hours, the children performed structured activities, including resting metabolic rate (RMR), Nintendo, arts and crafts, aerobic warm-up, Tae Bo, treadmill walking and running, and games. Activity energy expenditure (AFE) computed as EE — RMR was regressed against counts to derive threshold counts.

Seventy-four healthy 8th grade girls, age 13-14 yr, were recruited from urban areas of Baltimore, MD, Minneapolis/St. Paul, MN, and Columbia, SC, to participate in the study. Accelerometer and oxygen consumption [latin capital V with dot above]02) data for 10 activities that varied in intensity from sedentary (eg, TV watchThg) to vigorous (eg., running) were collected While performing these activities, the girls wore two accelerometers, a heart rate monitor and a Cosmed K4h2 portable metabolic unit for measurement of [latin capital V with dot above]02. A random-coefficients model was used to estimate the relationship between accelerometer counts and [latin capital V with dot above]02. Activity thresholds were defined by minimizing the false positive and false negative classifications.

These cut points were originally defined for 30s epochs and were scaled up.

The children performed a series of activities (lying, sitting, slow walking, fast walking, hopscotch and jogging) while wearing an Actigraph and a portable metabolic unit. The sample was divided into a developmental and a validation group. Random intercepts models were used to develop a prediction equation in the developmental group. The equation was assessed in the validation group by calculating limits of agreement (actual minus predicted energy expenditure). Thresholds for moderate and vigorous activity were derived by refitting the energy expenditure model with V02 as the outcome.

A calibration study was conducted to determine the threshold counts for two commonly used accelerometers, the ActiGraph and the Actical, to classify activities by intensity in children 5 to 8 years of age. Thirty-three children wore both accelerometers and a COSMED portable metabolic system during 15 min of rest and then performed up to nine different activities for 7 min each, on two separate days in the laboratory. Oxygen consumption was measured on a breath-by-breath basis, and accelerometer data were collected in 15-s epochs

Thirty preschool children wore an ActiGraph accelerometer (ActiGraph, Fort Walton Beach, FL) and a Cosmed portable metabolic system (Cosmed, Rome, Italy) during a period of rest and while performing three structured physical activities in a laboratory setting. Expired respiratory gases were collected, and oxygen consumption was measured on a breath-by-breath basis. Accelerometer data were collected at 15-second intervals. For cross-validation, the same children wore the same instruments while participating in unstructured indoor and outdoor activities for 20 minutes each at their preschool.

Note that these cut points were originally developed using 15s as the original epoch length. To obtain those cut points, reference the paper listed under Reference Research or divide the CPM values shown above by 4.

Note also that the Sedentary and Light cut points were added after consulting with K. McIver (author). The interpretation was based on a visual inspection of the data. Contact the author(s) for any further information.

These results were obtained from the 2003–2004 National Health and Nutritional Examination Survey (NHANES), a cross-sectional study of a complex, multistage probability sample of the civilian, noninstitutionalized U.S. population in the United States. Data are described from 6329 participants who provided at least 1 day of accelerometer data and from 4867 participants who provided four or more days of accelerometer data.

Though the full paper describes cut points for various ages, these cut points apply only to adults, 18 and older.

The sedentary level given here is assumed based on discussions with the authors and their affiliates. This level is not exclusively called out in the paper and should not be used as a validated measure of sedentary activity in adults. According to the paper, "For youth ages 6–17 yr, the age-specific criteria of the Freedson group, as published by Trost et al. (29), were used with thresholds for moderate activity of 4 METs and vigorous activity of 7 METs. This adjusts for the higher resting energy expenditure of children and youth (11,19). The specific criteria for each age are specified in the SAS code available at http://riskfactor.cancer.gov/tools/nhanes_pam (15)." Those levels are as follows:

Time Base Defined

During initialization, devices are given a "Start Time". That is, the time at which the device should start collecting data. The Start Time concept requires that the device itself be given a time base. When a device is initialized, ActiLife sends the current time to the device to serve as this time base. The device contains a very accurate crystal oscillator which begins incrementing the time base. This acts as the device's on-board clock until the device is reinitialized. Although there is some clock drift expected, it is minimal due to the high accuracy of the on-board crystal oscillator.

Atomic vs Local

Beginning with ActiLife 6.5.0, the initialization screen offers users the ability to select between a time base from the local computer or an atomic clock. The choice can be toggled by clicking on the hyperlink to the right of the "Device Time" option as shown below:

When local time is selected, the devices will be programmed with the computers local time as the time base. When the atomic time is selected, ActiLife attempts to capture the time base from the United States National Institute of Standards and Technology (NIST) time servers list located at http://tf.nist.gov/tf-cgi/servers.cgi. ActiLife first attempts to make connection with one of five random NIST servers from the list given in the hyperlink. If a time is available, the computer's local time-zone offset setting is used to set the correct hour. The minutes and seconds settings captured directly from the NIST server are used to complete the time base. If none of the five random servers are available, the time base will default to the local time.

This feature is helpful when synchronizing data collection across multiple sites.

We are often asked if the Epoch Length researchers use will affect the results they get from the various analysis calculations in ActiLife. The answer is yes; all calculations for Adults in ActiLife are based on 60 second Epochs; all calculations for children are based on 15 second Epochs. All calculations scale linearly up or down from these values for other Epoch lengths. The generally accepted industry standard age ranges for classifying someone as adult or as a child are as follows:

Infant = Less than 1 year old

Toddler = 1 to 2 years old

Pre-School = 3 to 5 years old

Children = 6 to 18 years old

Adult = 19 and older

To illustrate the difference that smaller Epochs can produce when scoring your data, we will compare two sample datasets; one that uses 10 second Epochs and one that uses 60 second Epochs (both samples are from the same dataset, just integrated at two different Epoch lengths).Smaller Epochs will produce a higher resolution dataset as there is more activity data for any given length of time (e.g. 5 minutes of 1 second Epoch data will yield 300 datapoints whereas 5 minutes of 60 second Epoch data will only produce 5). While you will get better resolution, the trade off in smaller Epochs is file size; smaller Epochs will create significantly larger files than larger ones.

The left side is 10 second epochs and the right side is 60 second epochs of the same data. For this example, we used the Freedson Combination (1998) EE formula and the Freedson Adult (1998) Cut Point classification (you can read more about the various algorithms available in ActiLife by going here). As you can see, for the cut point classification in 10 second epochs, there are 30 seconds worth of data in Vigorous activity levels and 30 seconds worth of Sedentary activity. In the 60 second epochs, there are 2 minutes worth of moderate activity. When it comes to kCals, for epochs less than 60 second seconds, we first scale the counts up to 60 seconds and then apply the Energy Expenditure (EE) formula. We then scale the result value back to the original Epoch value. As you can see, there is a significant difference in the resultant kCals. For this example, we used 100 kg as the mass for the EE algorithm.

There are similar differences in the METs, Bouts, and Sedentary Analysis due to the scaling.

We hope this helps to clarify how ActiLife treats different Epoch lengths.

In some cases, users may experience an error when updating to the latest release of ActiLife. The likely cause of this is a corrupt or malformed Preferences file that ActiLife is unable to import into the new version. You may see an error similar to the following screenshot:

To resolve this error, you will need to delete all previous preferences files and reinstall ActiLife. We will step through this procedure below.

Press and hold the Windows Key, then press the R key. This will open the Run Dialog box. If you are using Windows XP, enter "%USERPROFILE%\Local Settings\Application Data\ActiGraph\" (without the quotes) and click Ok. If you are using Windows Vista/7, enter "%LocalAppdata%\Actigraph\" (also without the quotes) and click Ok. This will open a Windows Explorer window where your User Preferences file(s) are stored. Simply delete the contents of the ActiGraph folder (do not delete the ActiGraph folder itself, only it's contents as shown below):

After you have deleted these folders, reinstall ActiLife according to the procedure outlined in the FAQ: "Installing/Reinstalling ActiLife 5.x/6.x"

If all went well, ActiLife should now open properly. Deleting these preferences files will reset ActiLife back to its defaults and you will need to re-enter any customizations that you have made for ActiLife (e.g. download locations, custom Cut Point sets, Date/Time Filters, etc) but you should now be able to open ActiLife as expected.

ActiLife 6.5 and later combined with wGT3X+ devices running firmware 3.0 or newer are capable of providing measurements of Heart Rate R-to-R intervals.

Collecting Heart Rate DataHeart rate data is captured from the wireless ANT+ enabled heart rate chest strap and is transmitted in real time to the wGT3X+. In order to capture heart rate data, the following must occur:

Initialize a wGT3X+. Be sure to enable "ANT+ Wireless" and "Heart Rate". Setting a PIN code is optional. The PIN code is required when communicating with the wGT3X+ from one of ActiGraph's mobile applications.

2. Position the ANT+ enabled heart rate strap around the chest area. Instructions for wearing the heart rate strap are available here.3. The wGT3X+ will pair over the ANT+ wireless channel with the first heart strap that it detects and will lock to that heart strap. As of ActiLife 6.5.0, there is no way to pair a single heart strap directly to the wGT3X+. This feature is coming soon.

If the wGT3X+ loses connection with the heart strap, it will begin searching immediately for another one. If no heart strap is detected, the wGT3X+ will wait 15 seconds before performing another scan. If no heart straps are detected, it will wait another 30 seconds before performing another scan. If no heart straps are detected, it will wait another 60 seconds before performing another scan. If no heart straps are detected after that waiting period, the wGT3X+ will search for nearby heart straps once every 2 minutes. This method prevents the wGT3X+ from completely depleting its battery life if the heart strap is no longer present.

4. Wear the wGT3X+ and the heart strap for the desired amount of time.

Downloading Activity and Heart Rate DataConnect the wGT3X+ to the computer running ActiLife and download the data. The *.agd file can be created at time of download. Be sure to check the "HR" option during download to ensure that the raw Heart Rate data collected on the wGT3X+ gets exported to the *.agd file for use in ActiLife.

Alternatively, the *.agd file can be created after download by clicking "finished downloading" from the device list or later by simply opening the raw *.gt3x file that is generated at time of download.

2. The resulting *.agd file will contain basic beats per minute data for each epoch. This can be viewed by opening the *.agd file in ActiLife (simply double-click on the *.agd file).

To view the Timestamp at millisecond resolution in MS Excel, right-click on the Timestamp column and choose "Format Cells..". Select the "Number" tab and choose the "Custom" option. Set the format to m/d/yyyy h:mm:s.000 and click "OK." This will set the Timestamp column to display the timestamp down to the millisecond resolution.

A sample file containing R-to-R data can be downloaded here.How Heart Rate Data is CapturedThe ANT+ heart strap polls the wearer's heart at a rate of approximately 4Hz. Each sample is transmitted to the wGT3X+ and is stored upon receipt.

After scoring files for Periodic Leg Movement (see How Do I Use the Periodic Leg Movement (PLM) tool?), click on the different sleep periods to view the graph and PLM stats for each sleep period.

GraphsClicking on a sleep period will load a PLM score graph. The green background represents the total time in bed (sleep period) as logged by the user. The graph will automatically show data 1 hour before the time in bed and 1 hour after the time out of bed. The blue horizontal line represents the onset threshold and the red horizontal line represents the decay threshold. The bar graph below the main activity graph gives users a quick reference to PLM bouts (bouts of activity that meet the qualifications of Periodic Leg Movement). Both graphs can be zoomed in or out simultaneously by dragging the mouse over the area of interest. To zoom back out, click the small circle in the lower left-hand corner of the graph until the desired zoom level is achieved.

Minutes of PLM Kicks - The total number of minutes of "kicks" or activity spikes that exceed the onset threshold AND the total minutes of consecutive "kicks" thereafter that exceed the decay threshold.

Number of PLM Kicks - The number of "kicks" or activity spikes that exceed the onset threshold AND the number of consecutive "kicks" that exceed the decay threshold.

Number of PLM Bouts - A count of the number of bouts of PLM that occurred during the selected sleep period.

Average PLM Bout Length (min) - The average length, in minutes, of all of the PLM bouts for the selected sleep period.

Minutes of PLM Bouts - The total time, in minutes, of PLM bouts. This time is different from the PLM kicks because kicks do not necessarily qualify as valid PLM.

Avg PLMS Intensity - The average intensity of the PLM during Sleep of all of the PLM bouts. Essentially, this is the duty cycle of all PLM bouts. Avg PLMS Intensity = total minutes of valid PLM / total PLM bout length.

For more information regarding the source of our PLM calculations, see "How were the Periodic Leg Movement algorithms derived?"

Two new advanced data exporting options were introduced in ActiLife 6.5.0

Exporting Only Valid Wear TimeFollowing a Wear Time Validation analysis, valid wear time can be exported directly to *.csv format for further analysis outside of ActiLife. IMPORTANT: While it is now possible to export wear time, it is advisable to simply work with the *.agd file in ActiLife. Keep in mind that Wear Time Validation simply flags non-wear epochs in the *.agd file, thus allowing ActiLife itself to perform further analysis which do not require any type of exports.To export valid wear times, perform a normal Wear Time Validation process by selecting the desired settings. Click the "Details" button next to the file from which you would like to export the valid wear time as shown below.

Clicking the "Export" button will reveal two options for exporting data from the Wear Time Validation Detail: "Day and Wear Information" and "All Wear Time Epochs"

Exporting Day and Wear InformationChoosing this option will export a *.csv of the Days and Wear/Non-Wear data that appears in the left-hand area of the Wear Time Validation details view. This includes a summary of Dates, Wear/Non-Wear Minutes, Wear/Non-Wear % and a detailed breakdown of the wear/non-wear time periods for the entire dataset. Simply choose a save location and a file name to create the export.

Exporting all Wear Time EpochsChoosing this option will generate an export of all wear time epochs. This includes, for a 60s epoch file, a minute-by-minute breakdown of all data for valid wear times for the entire dataset. Simply choose a save location and a file name to create the export.

Exporting Calculated Kcals and MET Rates per EpochFollowing a Data Scoring analysis, users may export calculated Kcals and MET rates. Click the "Details" button to the right of the file to open the "Data Scoring Details" view as shown below:

Click "Export All Epochs" to generate a *.csv containing the calculated kcals and MET rate for all epochs for that particular dataset.

You can convert AGD and GT3X+ files to one or more of the following: DAT, CSV and AGD. ActiLife alsohas other features for converting these files to specialized CSV file or other AGD files. 1. To begin, start byclicking on the "File" menu in ActiLife. 2. Then click on "Import/Export" to get a large selection of conversionoptions! 3. Select the option you wish to conver FROM and TO (ie AGD -> CSV).

Upon your choice, youwill be given the option to select one or more files to convert. You can convert many files at once! You will begiven a progress indication once you have submitted your file for conversion. Once complete, there will be a newset of files (along with your originals) that are now converted!

Note: In order to convert ActiLife AGD files to other formats, you must be using the FULL edition of ActiLife.

The Periodic Leg Movement (PLM) tool allows users to detect and score periodic leg movements in patients with PLM disorder or restless leg syndrome. Note: The PLM tool is only compatible with data collected from the legacy ActiSleep or the new ActiSleep+ monitor devices.

Initializing DevicesTo use the tool, initialize an ActiSleep monitor through the devices tab to start collecting data at the desired time. (Info about ActiSleep battery life) ActiSleep+ devices only have one option for initialization (30Hz). Legacy ActiSleep devices should be initialized to collect data at 1s epochs. During initialization, ActiLife gives users the option to print a sleep diary. The sleep diary can be used by patients who choose to wear the devices for several nights in their own homes or for doctors who are logging multiple patients' sleep behaviors. Patients should be instructed to use the sleep diary to keep track of the time in bed (TIB) and time out of bed (TOB) for purposes of analysis.

Attach the ActiSleep monitor to the top interior of the patient’s left and right feet around the ball of the foot as shown in the image below. This can be accomplished with bandage wrapping or with the straps provided with the ActiSleep monitoring package. Patients should wear the device during the entire duration of their total time in bed.

Downloading Devices

After patients have worn the devices for the desired number of sleep periods, collect the devices and download them using the Devices tab. Legacy ActiSleep monitors will produce an *.agd file on download. This file can be used for scoring PLM in the PLM tab. ActiSleep+ devices will create a *.gt3x file on download. This file contains the raw acceleration data collected by the devices and needs to be post-processed (i.e., converted to *.agd format) prior to PLM scoring. This can be accomplished by choosing “Create AGD File” from the download dialog as shown in the figure below. Select 1s epoch and 3 axes. The other options are optional, but “Low Frequency Extension” should not be selected.

After download, an AGD file will be created in the download folder which is also selected at the time of download.

Scoring the PLM DataTo score periodic leg movements, open the PLM tab and select the left and right leg *.agd files. Click the “Add Sleep Periods” button and select the entry method.

Add Period Using Bed Times– Use this option to manually enter in times as recorded by the doctor or patient on the sleep diary.

Import from Separate AGD File– Use this option to import sleep times from another AGD file that already contains the same sleep times.

Enter Period Graphically- coming soon)

Select which axis or axes to use to score the PLM data. The Vector Magnitude option combines all three axes using the vector magnitude formula SQRT( (Axis 1^2) + (Axis 2)^2 + (Axis 3)^2).

Select the thresholds for detecting legitimate “movement”. Note: these thresholds only set the level at which PLM activity occurs. ActiLife’s built-in algorithm determines whether or not the movement qualifies as PLM.

Selecting the different sleep periods in on the left-hand side will populate the graphs and results for that corresponding period for both left and right side files. See “How Do I Interpret the PLM Output?” for further information.

With the introduction of 3-axis accelerometers, it is now possible to expand beyond gross measurement of physical activity and classify activity types. The ActiGraph GT3X Triaxial Activity Monitor takes a first step in this direction with an Inclinometer feature that indicates whether a subject is standing, sitting or lying down when the device is worn at the hip as well as indicating that a device is not being worn at all. This feature provides an additional data point for interpretation of activity levels by researchers and health professionals and is also included in the ActiSleep and 3-axis ActiTrainer Research Model devices.

In the absence of activity, a 3-axis accelerometer senses the acceleration due to gravity. This value is effectively a down vector from which the orientation of the device (with the exception of heading) is determined. Equations 1 and 2 define the calculation used to obtain two important angles for determining a subject's posture where x, y, and z represents the acceleration along each axis.

Equation 1:

Equation 2:

When worn on the hip and perfectly vertical, the y-axis alone should contain the total acceleration due to gravity. As a subject inclines, the offset angle (θy) increases. If the device is not being worn, then one expects the z-axis to reflect the total acceleration due to gravity as the device rest on a table-top for example. Therefore, the addition of the z-axis offset angle (θz) is required to distinguish between lying and off. Figure 1 contains examples of this y-axis offset angle in the standing (top-left), sitting (top-right), lying (bottom-left), and z-offset angle in the off (bottom-right) positions.

Figure 1

Optimal angle thresholds are required to correctly estimate the posture for the widest range of users. The acquisition of this data involved collecting 30 Hz raw accelerometer data using a GT3X Activity Monitor from numerous subject's for analysis. Each subject was instructed to stand, sit and lie down for 5 minute periods for a total of 15 minutes worth of data. Each 30 Hz sample was then categorized and a computer script was used to determine the optimal θy value for standing, sitting and lying by minimizing the error between the known inclination and the estimate. Raw data was collected using several GT3X devices with and without belt clips and pouches with belt loops and the same script was used to determine the optimal θz value.

When activity exceeds six counts per second, the user is assumed to be standing due to the high activity values and thus the inclination angles are ignored. Otherwise, θy < 17º is considered STANDING, 17º < θy < 65º is considered SITTING, and θy > 65º is considered LYING unless θz < 22º when the unit is OFF. Internal tests have regularly surpassed 95% accuracy with exceptions for outliers with posture at the extremes (e.g. one subject sat up as straight as to indicate standing while sitting). All parameters of the algorithm will be configurable by the ActiLife software in case broader research finds more optimal values.

Estimation of human position using 3-axis accelerometers adds further value to the already solid actigraphy performance of the ActiGraph GT3X. With knowledge of a subject’s disposition, researchers and health professionals can garner further insight from the already accurate activity levels, energy expenditure and step counting. Detecting off, lying, sitting and standing positions is just a first step in even more detailed activity classification to be achieved by the research community using high data rate 3-axis activity monitors from ActiGraph.

In order to install ActiLife properly, you must have administrative rights on the computer on which you wish to install ActiLife and due to a recent Microsoft update, you may have to allow the installer to have full permissions to install all required components. First, please make sure you have downloaded the latest installation executable for ActiLife 5.x/6.x. You can get these from the following location:

Please note: ActiLife MUST be installed locally. Roaming Profiles or Profile Folder redirection are NOT supported.

Also Note: ActiGraph is no longer able to offer support for ActiLife software prior to version 6.0, released in February 2012. This policy, effective December 31, 2012, was implemented to allow our technical and development teams to focus exclusively on supporting the new and improved ActiLife 6 platform. Those customers operating ActiLife versions 4 and 5 will be able to continue to use their software, however ActiGraph no longer provides assisted support, program updates, new bug fixes, automatic device firmware updates and online technical content updates. In order to reduce the risk of potential device and/or data issues related to outdated software, we strongly encourage our customers to upgrade to ActiLife 6. You can upgrade by visiting our online store (click here) or by contacting our Sales Department by emailing them at sales@actigraphy.nl

You will need to download and save this file to your computer. Once it is downloaded, right-click on the file and choose "Properties". On the General tab, near the bottom of the window, you may see a heading that says "Security" with a button next to it that says "Unblock". It should look similar to this screenshot:

Click on the Unblock button and then click Apply and then Ok. If you do not see this button, the security update in question has not been installed and you may proceed with the next step of the installation.

Now right click on the file one more time and choose "Run as..." (for Windows XP) or "Run as Administrator" (for Windows 7/Vista). If prompted for a user account, choose Current User and Uncheck the box below that that says "Protect my computer...". Then click Ok again. This will allow the installer to properly install of the necessary components for ActiLife to work. If you do not perform these steps, it may appear that ActiLife has successfully installed but critical components will have failed to be installed.

If you see a prompt like the one below, asking to install drivers, tick the box that says "Always trust software from ActiGraph" and click the Install button.

After the installer has completed, uncheck the box that says "Run ActiLife" and click Finish. Restart your computer to ensure that any required services start correctly. Once Windows has booted back up to the Desktop, you should be able to open and use ActiLife. If this is a new installation of ActiLife, you will be prompted to enter your Product Key when you open it for the first time.

Please note: running ActiLife inside of a virtual environment like VMWare, VirtualBox, or Parallels is NOT supported (BootCamp on a Mac should work fine as you are technically booted into a native Windows environment when doing that). At this time, ActiLife requires that you are using Windows in a native, non-emulated configuration.

If you do not have your Product Key or if you need any further assistance, please visit our support portal at http://www.actigraphy.nl/en/contact. From there, you can view many helpful FAQ's and Troubleshooting Articles; log in to check any existing Support Requests; or create a new Support Ticket if you are unable to find a solution. You can also email us at support@actigraphy.nl.

Users can set date and time filters within the Data Scoring tool. These filters allow users to perform their analysis on specific dates and/or periods of time. Date and time filters are stackable, meaning that users can create multiple overlapping filters (e.g., Weekends and Mondays). To create a new filters, click the "+" icon in the Date and Time Filters section of the Data Scoring tab as shown below.

Create a filter using the date and time filter dialog.

Filter Name - The name of the filter as it will appear in the filter list.

Date Options - The specific day or groups of days (weekends/weekdays) that the filter should include.

Start/Stop Times - The times of day the filter should include.

Click Save.

A list of filters will appear as they are added.

To exclude a filter from the current analysis, uncheck the box to the left of the filter. Checked filters apply to all files in the Data Scoring grid and area also applied when exporting the data to Excel (using the export option on the bottom right). ActiLife remembers filters, even when the application is closed and reopened.

FunctionalityThe Bout Detection tool can be used to identify multiple types of "activity bouts." Using this feature, ActiLife can detect the number of occurrences of a bout (or multiple bouts), the average length of the bout(s), the total time spent in the bouts, and the total count level of the bouts. Once the calculation is complete, a breakdown of each bout can be reviewed.A bout of activity can be defined using the following parameters:

Minimum Length (in minutes or epochs) - This defines the minimum required length of the bout

Minimum Counts (per minute or epoch) - This sets the lower threshold required to detect the start of (or continuation of) a bout. For example, the default bout set has a minimum level of 1952 counts per minute. Once this level is exceeded (assuming the 'Max Counts' level is not exceeded), that minute counts as the beginning of a potential bout.

Max Counts (per minute or epoch) - This sets the upper threshold required to detect the start of (or continuation of) a bout. If a count value exceeds this value, it does not count toward the creation (or continuation) of a bout.

Drop Time (per minute or epoch) - The Drop Time value acts as a tolerance. Once a bout is initially detected (e.g., 1952 counts is detected), the bout is allowed to experience no more than the number of "drop time" epochs or minutes outside of the minimum and maximum count levels. As long as the number of "Drop Time" epochs or minutes do not exceed the specified threshold, the bout will continue. If this number is exceeded, the bout ends (or never begins if the minimum length has not been encountered).

DefaultsBout detection defaults were derived from two different sources. The United States Centers for Disease and Control Prevention (CDC) recommend that adults obtain 150 minutes of moderate to vigorous intensity activity per week (reference). Recommendations go on to state that these requirements can be obtained in 10 minute intervals (hence the 10 minute default for bouts).

In addition, the source GPS data will be saved in the actigraphy data file (*.agd). This eliminates the need to re-import the GPS data for future re-calculations. The correlation process does not affect the actigraphy data in any way.Using the GPS CorrelatorTo begin, click the "Select Dataset" button and select an *.agd file that contains epoch-level actigraphy data from an ActiGraph device that was worn simultaneously with a GPS logging device. Any epoch level greater than 1 second is allowed. If this file contains GPS correlated data, the GPS correlator tool will give the option to delete or select another GPS dataset to replace the existing GPS data.

In the GPS Data section, select a device type and browse to the GPS file that contains the time-stamped GPS data. ActiLife will prompt with an alert if this data does not align with actigraphy data in the *.agd file selected above.

In the results section, select the desired output(s). "Create a CSV correlation output" will create a CSV file that contains both the epoch level actigraphy data and GPS data in separate columns, correlated by timestamps. The "Create an intensity map (KMZ) output" option will create a KMZ file which contains correlated GPS data points and cut point intensity levels (see image below). Users can open the KMZ file in various programs including Google Maps. Select the desired cut point set (defaults in ActiLife - read more here) or create a new custom set by clicking the "edit" link to the right of the cut point selection combo box. Checking the box "Open with Google Earth" will automatically load the intensity map in Google Earth after the correlation is complete (Google Earth must be installed on the same computer as ActiLife).

The dataset (*.agd file) will be modified to include ALL of the GPS data. For example, if GPS data and actigraphy are at different resolutions (e.g., 10s epochs and 1s GPS data), the correlator tool will insert ALL of the GPS data into the *.agd file. The original actigraphy data will not be affected. ActiLife will properly handle the file regardless of the presence of the GPS data. The GPS data can be deleted from the *.agd file at any time.

If selected, a CSV output will be generated. Epoch-level actigraphy data always takes priority during the correlation process. If present, excess data from the higher resolution GPS file is discarded. For example, a 10s epoch actigraphy file correlated with a 1s epoch GPS file will result in a 10s epoch CSV file. The correlator tool will use the closest matching GPS timestamp to correlate with each 10s epoch actigraphy count. In addtion, GPS data points that are not within the timespan of the first and last activity data point are not output.

If selected, a KMZ output file will also be generated. Similar to the generation of a CSV file, the KMZ output will be driven by the epoch-level actigraphy resolution. A 10s epoch actigraphy file correlated with a 1s epoch GPS file will result in a 10s resolution KMZ file; excess GPS data will be discarded in this file.

Sedentary Analysis DescriptionThe Sedentary Analysis feature identifies bouts of low activity in *.agd datasets. Following Wear Time Validation, this feature will identify details about a subject's sedentary behavior (rather than identifying non-wear as sedentary time). When enabled, this feature yields following outputs in the Data Scoring tool:

Summary Screen

Total Sedentary Bouts - the number of sedentary bouts detected in the entire dataset

Total Length of Sedentary Bouts - The total sedentary time detected in the entire dataset.

Max Length of Sedentary Bouts - The length of the longest sedentary bout in the entire dataset

Min Length of Sedentary Bouts - The length of the shortest sedentary bout in the entire dataset

Daily Average of Sedentary Bouts - The Total Length of Sedentary Bouts divided by the total valid days in the dataset

Total Sedentary Breaks - The total number of breaks in sedentary activity for the entire dataset.

Total Length of Sedentary Breaks - The sum of all the times between sedentary bouts in the dataset.

Average Length of Sedentary Breaks - An average of all of the times between sedentary bouts (Total Length of Sedentary Breaks/Total Sedentary Breaks)

Max Length of Sedentary Breaks - The length of the longest sedentary break in the entire dataset

Minimum Length of Sedentary Breaks - The length of the shortest sedentary break in the entire dataset

Daily Average of Sedentary Breaks - The Total Length of Sedentary Breaks divided by total valid days in the dataset

An example Summary Screen is shown below:

Details View - After clicking "Details" button

Total Sedentary Bouts - the number of sedentary bouts detected in the specified day/hour

Total Length of Sedentary Bouts - The total sedentary time detected in the specified day/hour

Max Length of Sedentary Bouts - The length of the longest sedentary bout in the specified day/hour

Min Length of Sedentary Bouts - The length of the shortest sedentary bout in the specified day/hour

Daily Average of Sedentary Bouts - The Total Length of Sedentary Bouts divided by the total valid days in the specified day/hour

Total Sedentary Breaks - The total number of breaks in sedentary activity for the specified day/hour

Total Length of Sedentary Breaks - The sum of all the times between sedentary bouts in the specified day/hour

Average Length of Sedentary Breaks - An average of all of the times between sedentary bouts (Total Length of Sedentary Breaks/Total Sedentary Breaks) in the specified day.

Max Length of Sedentary Breaks - The length of the longest sedentary break in the specified day/hour

Minimum Length of Sedentary Breaks - The length of the shortest sedentary break in the specified day/hour

A sample "Details" view is shown below:

Sedentary Analysis SetupTo setup and run a sedentary analysis, check the box beside the "Sedentary Analysis" option in Data Scoring and click "edit..." to define sedentary analysis parameters. A sample window and a description of the parameters is given below.

Bout Name - Uneditable

Length

Minimum - The minimum allowable amount of consecutive time that can be considered a sedentary bout

Use Maximum - (optional) The maximum allowable amount of consecutive time above the minimum that can be considered a sedentary bout

Drop Time - The tolerance, or allowable amount of total time (not consecutive) that can break a sedentary bout.

Count Levels

Minimum - The minimum count level to be considered a sedentary bout

Use Maximum - (optional) The maximum count level to be considered a sedentary bout

Use Vector Magnitude - This option uses the Vector Magnitude of all three axes when considering the count level values defined in the Minimum and Maximum.

Ignore First Sedentary Break of Each Day - If this option is checked it will not factor the time between the first sedentary bout of the day and the last one on the day before when calculating stats. So if you have a sedentary bout from 9:00pm to 9:30pm one night and then the next morning you have one from 9:30am to 10:00am, ActiLife does not assign the time from 9:30pm to 9:30am as a sedentary break.

Example: Consider a 60s epoch file. If a 10 minute minimum is set, a drop time of 2 minutes is entered, and a maximum count level is 99, the following sequence would be considered a sedentary bout:

Epoch time episodes refer to the data within the epoch according the time stamps below. You'll see an example where data in 15 seconds epochs are collected, started at 9:00:00

Data from 9:00:00 – 9:00:14 will be marked as 9:00:00Data from 9:00:15 – 9:00:29 will be marked as 9:00:15Data from 9:00:30 – 9:00:44 will be marked as 9:00:30Data from 9:00:45 – 9:00:59 will be marked as 9:00:45Data from 9:01:00 – 9:01:14 will be marked as 9:01:00Data from 9:01:15 – 9:01:29 will be marked as 9:01:15

ActiLife requires that the Microsoft DOT Net framework, v3.5, SP1 be installed on the computer where you will be installing ActiLife. This is a free download and should be included in Windows Vista/7 but may not be installed on Windows XP. You can find this at the following location:

No, at the moment is software from ActiGraph incompatible with Macintosh computer systems. ActiGraph is currently developping the possibility to connect both. Of course we will keep you informed through the website about these developments.

Can I use ActiLife 5/6 and older software versions together on 1 computer?

Yet, it is possible to use ActiLife 5 or 6 and older ActiLife versions on one computer. However, new devices and older software versions cannot communicate. The following Macros and functions are usable in this case:

There are two editions of ActiLife; a Full Edition and a Lite Edition; Using the Full Product Key will enable all of the data analysis features of the software and can be activated on one (1) computer at a time (this number increases to 5 for Enterprise License users). If the Lite Product Key is used, only the ability to download or initialize devices will be enabled. The Lite Product Key may be used on up to 5 additional computers (15 computers for Enterprise License users). This may be used in addition to the one copy of the Full License described above. This is useful for the purposes of centrally processing the collected data using the Full Edition while the Lite Edition can be used to collect data in the field which can then be sent to the Full user for analysis. Both editions cannot exist on the same computer.

We also have implemented portability into our licensing. It is not uncommon for a researcher to temporarily need the Full Edition if they are onsite for analysis. In order to move your installation of ActiLife from one computer to another, you must first Deactivate the license on the old computer. This will release a license and allow you to Activate it on the new PC. To do this, go to the old computer, open ActiLife, and click on Help. There is a link that says "Activation Details"; click on that and make a note of your Product Key as you will need this to reactivate the software on your other computer. Then close this window and click on Deactivate. Then click the Deactivate ActiLife button and again, follow the prompts. ActiLife will then close and you will then be able to install and activate ActiLife on a different computer. This same mechanism allows you to easily switch between the Full and Lite Editions as needed.

This procedure also applies to computers that have had Windows reinstalled or if you wish to switch between the Full and Lite version of ActiLife (and vice versa). You should deactivate ActiLife before reinstalling Windows to reduce your usage. You should then be able to reinstall ActiLife and activate it with no issues. We understand that this is not always possible (hard drive crashes, system failures, etc) but when possible, following these steps will ensure minimal interruption for users of ActiLife.

You can also use this procedure to switch between the Full and Lite editions on the same computer.

The Lite and Full version of the software are the same, the only difference is the Product Key used to activate the software. The Full Edition is required to manipulate or analyze the data collected from our devices.

There are two editions of ActiLife; a Full Edition and a Lite Edition; Using the Full Product Key will enable all of the data analysis features of the software and can be activated on one (1) computer at a time (this number increases to 5 for Enterprise License users). If the Lite Product Key is used, only the ability to download or initialize devices will be enabled. The Lite Product Key may be used on up to 5 additional computers (15 computers for Enterprise License users). This may be used in addition to the one copy of the Full License described above. This is useful for the purposes of centrally processing the collected data using the Full Edition while the Lite Edition can be used to collect data in the field which can then be sent to the Full user for analysis. Both editions cannot exist on the same computer.

We also have implemented portability into our licensing. It is not uncommon for a researcher to temporarily need the Full Edition if they are onsite for analysis. In order to move your installation of ActiLife from one computer to another, you must first Deactivate the license on the old computer. This will release a license and allow you to Activate it on the new PC. To do this, go to the old computer, open ActiLife, and click on Help. There is a link that says "Activation Details"; click on that and make a note of your Product Key as you will need this to reactivate the software on your other computer. Then close this window and click on Deactivate. Then click the Deactivate ActiLife button and again, follow the prompts. ActiLife will then close and you will then be able to install and activate ActiLife on a different computer. This same mechanism allows you to easily switch between the Full and Lite Editions as needed.

This procedure also applies to computers that have had Windows reinstalled or if you wish to switch between the Full and Lite version of ActiLife (and vice versa). You should deactivate ActiLife before reinstalling Windows to reduce your usage. You should then be able to reinstall ActiLife and activate it with no issues. We understand that this is not always possible (hard drive crashes, system failures, etc) but when possible, following these steps will ensure minimal interruption for users of ActiLife.

You can also use this procedure to switch between the Full and Lite editions on the same computer.

The Lite and Full version of the software are the same, the only difference is the Product Key used to activate the software. The Full Edition is required to manipulate or analyze the data collected from our devices.

We sometimes get inquires about determining the actual angle of inclination that our devices measure. Some researchers may need more information than just the category of inclination (i.e. sitting, standing, lying, or off). While this information is not available in ActiLife itself, you can determine this from the raw data by using some trigonometric calculations as explained below. ActiLife does not provide this information as ActiLife currently only analyzes Epoch Data.

The inclination angle(s) can be determined from the raw data. Both pitch and roll may be calculated but at this time, there is not enough data to determine yaw/heading.

Note that the Analog Devices paper assumes that measured acceleration is only due to gravity. So, any accelerations due to the subject must be filtered out. This can be done using a digital low-pass filter since gravity remains constant while subject accelerations change rapidly.

Tuning of a low-pass filter for gravity is dependent upon your desired results. A high-order filter with a low cutoff frequency will yield very smooth results, but will respond slowly to changes in inclination. The opposite is true as the frequency is increased. So, you have to weigh reducing noise against responsiveness. You will need to construct your own low-pass filter.

Lux, or ambient light, is measured by ActiGraph’s ActiSleep, ActiSleep+, ActiTrainer, GT3X+, and our new wireless wGT3X+ and wActiSleep+ devices. Ambient light may affect subject sleeping habits and thus is a useful tool in analyzing circadian rhythms and sleeping patterns. Lux data is stored once per epoch. For GT3X+/ActiSleep+/wGT3X+/wActiSleep+ devices, Lux data is stored once per second. When converting a GT3X+ raw file into an accumulated *.agd format with epoch lengths greater than one second, the lux values for that epoch are averaged. An estimate of industry accepted lux values is shown below. The ActiTrainer, GT3X+, and wGT3X+ devices are capped at a maximum value of 2500; the ActiSleep, ActiSleep+, and wActiSleep+ are capped at 6000:

Note that these levels are only estimates and are not meant for exact interpretation of the light detected by the device. Lux values reported by ActiGraph devices are intended as a general guideline for average light intensities.

Overview: I Have My Device And ActiLife Is Installed; Now What?
1. the device tab

We will begin with the very first thing you see when you open ActiLife; the Devices Tab. This is where you will perform all interactions with your Devices; Initialization, Downloading, Firmware Updates, etc. You will notice that there are a number of columns that provide information about the device itself; the Serial Number, the Battery Level, Sample Rate of the data being collected, etc. There are also several buttons along the top of the Grid that allow you to perform actions on the devices that are connected.

The Initialize button is used to prepare the device for data collection.

The Download button is how you retrieve the data from the device and get it onto your computer for analysis.

The Refresh and Refresh All buttons allow you to reload the device grid to make sure all of the devices are visible and reporting the correct information. it's the same idea as refreshing a webpage; you are just reloading information that is being reported by the connected devices to ActiLife. Refresh only refreshes selected devices; Refresh All refreshes the entire device grid.

The Identify button allows you to cause a device's LED to flash so that you know which device you are about to manipulate. This is useful when you have multiple devices connected but perhaps only wish to download or initialize a single device. Using the Identify function will help you make sure you are performing whatever actions you want to on the correct device.

For the purposes of this tutorial, we will not go into detail explanations of the Wireless Functionality. That will be covered in another tutorial that will be posted at a later time.

Overview: I Have My Device And ActiLife Is Installed; Now What?
2. The Wear-Time Validation Tab

The next step after retrieving your data is to begin analyzing it. The first step in this process is to validate the data for Wear and Non Wear-time. Wear-Time Validation (also known as WTV) is the method by which ActiLife flags Epoch data as Wear-time (i.e. yes, the device was being worn and the subject was doing stuff) or Non-Wear time (i.e. no, the device was not being worn or the subject was not doing anything). This is achieved by applying an internally developed algorithm along with other customizable criteria to the dataset. We will not go into detail about what each of these criteria are here but you can find out more about them by viewing the FAQ articles posted here.

On the left side of this tab, you will see the various criteria that you can adjust for your specific study (in most instances the defaults are fine). The buttons along the top of this tab allow you to add or remove Dataset(s). Simply clicking on the Calculate button will analyze the dataset using the selected criteria. You can use the scroll bar at the bottom of the window to see the results for the entire dataset. If you would like to view the results down to the Epoch level, click on the details button next to the dataset.

Overview: I Have My Device And ActiLife Is Installed; Now What?
3. The data scoring tab

Once you have validated your data, the next step is to actually analyze it. ActiLife's Data Scoring tool has the ability to analyze a dataset for all of the primary criteria relevant to actigraphy; Energy Expenditure, METs, Cut Points, Bouts, Heart Rate, and Sedentary Analysis. You can read more about these various criteria here.

Along the left side of the Data Scoring Tab, you will see the various criteria you can use. You can select different algorithms from the pull down menus and in the case of Cut Points, Bouts, and Sedentary Analysis, even create your own. You can also add Data/Time filters that will allow you to specify that only certain days or time periods be included in the analysis.

The buttons along the top of the window will allow you to add/remove datasets and to customize which columns are displayed. The number of currently loaded datasets is also display in this area. The buttons along the bottom allow you to perform the analysis and to export the results out to either a CSV or Microsoft's Excel XLSX file format.

Once the data has been scored, you can click on the details button (next to the dataset filename) to see a breakdown of the data down to the Epoch level. If the data was scored for Sedentary Analysis and/or bouts, you will also be able to see a breakdown of those as well.

To add your own custom Cut Point sets, simply click on the edit button next to Cut Points to open the Cut Point Editor. You cannot edit the existing Cut Points but you can add a new one based on an existing one and then adjust it to suit your requirements.

You would do the same thing to create your own Bout definitions.

You can also adjust the options for Sedentary Analysis.

For many users, this is as far as they might go in analyzing their data. However, there are several other analysis tools in ActiLife for users with other requirements such as Sleep Analysis and PLM. For an explanation of the Sleep Analysis Tab, click here.

Overview: I Have My Device And ActiLife Is Installed; Now What?
4. The Sleep Analysis Tab

The next tab we will explore is the Sleep Analysis Tab. Unlike the WTV and Data Scoring Tabs, you can only use this tool with one dataset at a time. This is simply due to how this data is displayed using graphs as opposed to rows and columns of data. There is simply no practical way to display all of this information for multiple files on the same screen.

There are a number of functions here to make note of: the Select Dataset button allows you to load your data into the Sleep Analysis tool. You also have the option of select either a 24 or 48 hour scale for the graph. The AutoScore feature will attempt to automatically detect Sleep Periods. You can also switch between the Cole-Kripke and Sadeh sleep algorithms. More information on these algorithms can be found here. Using ActiSleep/ActiSleep+ devices also allows the ability to create custom sleep algorithms.

Once you have either Auto-scored your sleep periods or manually entered them, you will have the option to export the scored data as a PDF or as a CSV file. Please note below that after I have entered my sleep times, there is information about those sleep time listed in the lower right pane of the Sleep Analysis tool.

You can also switch the view to the ActoGram View which is helpful identifying sleep patterns over several days or weeks.

Whichever view you use, you can see more detailed information about the scored sleep periods by clicking on the "Show Sleep Epochs" button. YOu can also export using the Save Sleep Report button.

Please note; the Auto Sleep Score, Custom Sleep Algorithm, ActoGram View, and Clinical Reports functions in ActiLife are only available for our ActiSleep/ActiSleep+ devices. However, these features can be enabled for other devices for a small fee. Please contact our Sales team at sales@actigraphy.nl for more information.

Next we will go over the Periodic Limb Movement (or PLM) Analysis tool.

One of the most common questions we often get from our customers is "How do I use my devices and ActiLife?" and "I really have no idea where to even start. Can you please help me?". The purpose of this article is to hopefully provide a step-by-step guide to get the most from our state of the art Activity Monitors and the powerful suite of Data Scoring and Analysis tools found in ActiLife. For the purpose of this tutorial, I will be using ActiLife 6.4.2 installed on 64-bit Windows 7 Professional and one of our new ANT+ Enabled wireless devices, the wActiSleep+. While some of the options available to you may differ depending on the device you are using and the version of ActiLife you have installed, it should all be very similar.

If you have not yet installed ActiLife, please do so by following the steps outlined in this FAQ article.

The very first thing you need to do is make sure your device is charged. All devices ship from us in a partially charged state and in "Halt" mode. This simply means that the device is in a low power state and needs to be initialized before being deployed (think of it as similar to needing to fully charge and activate a new cell phone before you can use it). Charging our devices is a very simple process; simply connect the device to an available USB port on your computer using a USB cable. If there are no problems with the device, the USB port or the cable itself, the LED on the device should begin to blink, indicating that it is charging. Please refer to this FAQ for more information on what the various LED patterns mean. Fully charging a device can take 2 to 3 hours on average, depending on the level of charge on the battery. Once the device is fully charged, the LED will remain solid while connected to your computer.

Once ActiLife has been installed and activated and the device has fully charged, we're ready to get started. There are five basic steps involved in this process; Initialization, data collection, downloading that data, validating wear-time, and analyzing the data that you have collected. We refer to these 5 basic steps as the ActiLife WorkFlow Process.

Initialization refers to the process of preparing the accelerometer to collect activity data from your research subjects.

Data Collection refers to the act of collecting activity data from your subjects.

Downloading means retrieving the collected data from the device and getting it onto your computer for analysis.

Validating Wear-time refers to the process of flagging activity data as either Wear-Time (the subject was wearing the device and engaging in some sort of activity) or Non-Wear Time (the subject was either motionless or not wearing the device) using internally developed algorithms.

We will go into details on all of these steps later in this FAQ. While you may not be responsible for all of these steps in your research (i.e. you may only be downloading devices or scoring data collected from other researchers), understanding the work flow is beneficial because you will understand how each step is an important part of the process.

At the end of this overview of ActiLife, we will take a step by step walk through of the actual workflow process from start to finish using Epoch data created from sample activity data so that you will have a solid understanding of this Work Flow Process.

First, it might be helpful to go over the various tabs in ActiLife and briefly explain how you will use the different areas regardless of whether you are doing everything yourself or if you are only responsible for one particular aspect.

When exporting raw *.gt3x files to *.csv (by using the Raw to Raw function from the Import/Export Menu), users have the option to "Include Timestamps". This option places a new column in the resultant *.csv output that includes the full date and time stamp, to the millisecond.

You can also set this as the default behavior by going to Tools, then Options, and clicking on the Downloads Tab:

A "dot" notation is used to indicate the millisecond timestamp for each record in the raw data. Below is an example portion of a raw data export recorded at 30Hz.

Note that the addition of the timestamp can more than double the overall size of the *.csv export.Date formatting will be specific to the local exporting computer's culture settings. The time portion, however, will always be a 24-hour (to the millisecond) representation.

Where did we get our defaults for the Wear Time Validation algorithms?

The defaults for Floating Window Wear Time Validation were not derived from any specific source. However, the NHANES SAS code and comments (available at http://riskfactor.cancer.gov/tools/nhanes_pam/) from the 2003-2004 NHANES data set were used to derive the adjustable parameters. While the 60s consecutive "zeros" and 2 minute "Spike Tolerance" defaults do match this documentation, by default the "Spike level to stop" option is disabled. In order to match the NHANES SAS code, this option should be enabled and set to 100. If enabled, it is defaulted to 100.

Where can I find documentation for the Sadeh and Cole Kripke algorithms?

There are two built-in algorithms available in the Sleep Scoring tool to users. Both algorithms score individual epochs as either sleep or non-sleep. Based on the results of the algorithm, ActiLife is able to discern Sleep Onset, Latency, Total Sleep Time (TST), Wake after Sleep Onset (WASO), Number of Awakenings, and Efficiency.

The Sadeh algorithm was derived from fundamental research performed by Avi Sadeh, Katherine Sharkey, and Mary Carskadon entitled Activity Based Sleep-Wake Identification: An Empirical Test of Methodological Issues. This paper is available on ActiGraph's research database here. This algorithm is primarily used for younger adolescents as most of the research was performed on children and young adults.

The Cole Kripke algorithm was derived from research performed by Roger Cole, and Daniel Kripke in the technical note Automatic Sleep/Wake Identification from Wrist Actigraphy. This paper is also available on ActiGraph's research database here. This algorithm is primarly used to score adult populations.

Users wishing to create their own sleep algorithm based on the techniques used in the Sadeh and Cole-Kripke may create their own algorithm using the "Custom Sleep Score Algorithm Builder".

The Periodic Leg Movement tool in ActiLife utilizes some algorithms to yield several output parameters related to the detection of Periodic Leg Movement or PLM. The following outline describes those terms and the references we used to develop the algorithm related to those terms.

PLMS - Periodic Leg Movement during Sleep. The definition of periodic leg movement used in ActiLife is the same as the definition found in the research paper by Gschliesser, et.al., PLM detection by actigraphy compared to polysomnography: A validation and comparison of two actigraphs. Accordingly, PLM are defined as "a sequence of four or more leg movements separated by at least 5 (and not more than 90) seconds with a duration between 0.5 and 5 seconds." Since the minimum epoch allowed in ActiLife is 1s, our criteria for defining a PLM is a duration between 1 and 5 seconds. A minimum of four "kicks" (or, leg movements) are required to start a PLM bout (or PLMS).

HREE stands for "Heart Rate Energy Expenditure." This feature works with *.agd files that contain heart rate data (beats per minute) collected from a wireless heart rate transmitter. Compatible ActiGraph devices include the ActiTrainer (obsolete), the wGT3X+ and the wActiSleep+ monitors. "w" devices collect heart rate data over a wireless ANT+ channel from the ANT Heart Rate Monitor sold through the ActiGraph online store.

When an *.agd file contains heart rate information, the HREE feature uses that heart rate information to estimate energy expenditure during times of low ambulatory movement. Basically, this algorithm examines the subject's heart rate during high activity and during low activity (to obtain a baseline) to determine an equation that can be used whenever activity levels are low and heart rate levels are high. This equation can be used by the researcher to estimate calorie burn during non-ambulatory times (e.g., when someone is lifting weights or cycling).

Note: This method is not validated by any independent research organization. It was derived by ActiGraph's R&D department and has not been studied on large populations.

The HREE tool calculates 5 variablesADL Heart Rate - This is the Average Daily Living Heart Rate for the data set. Ever 60s epoch with a count level greater than 100 counts per minute (CPM) and a heart rate between 41-79 beats per minute (bpm) gets averaged. This average represents the Average Daily Living Heart RateAvg Active Heart Rate - The average heart rate for all 60s epochs with greater than 1951 CPM. The heart rate value for the epoch must exceed 79 bpm in order to be counted as part of the averageHeart Rate Delta - The difference between the Average Active Heart Rate and the ADL Heart Rate.Avg Active Caloric Expenditure - The average calorie burn (in kcals) for all epochs with a heart rate value >79 bpm and count levels >1951 cpmCalibration Ratio - The slope of the heart rate - energy expenditure line (Avg Active Caloric Expenditure-Average Baseline Calories)/(Avg Active Heart Rate-ADL Heart Rate)

Note: The Average Baseline Calories value is not calculated as part of the HREE output but is just an intermediate variable. This value represents the average calorie burn (in kcals) for all epochs with a heart rate value between 41-79 bpm and count levels >100 cpm

In order for the HREE tool, the *.agd data set must contain at least one epoch where axis1 > 100 cpm AND HR is >= 41bpm and <= 79bpm AND at least one epoch where axis1 > 1951 AND HR >= 80.

The "Worn on Wrist" option allows users to tell ActiLife that the device was worn on the wrist and that the calculations in Data Scoring should be scaled appropriately. At the time of this writing, there are no validation studies available that illustrate how to properly scale wrist worn devices to yield kCal information. Waist-worn kCal calculations are still the defacto standard and research studies have proven the reliability. That said, ActiGraph applies the following piece-wise scaling to wrist-worn devices based on internal research and development:

Sleep efficiency or 'sleep efficiency index' is the amount of sleep in the nightly period when the person was really asleep, so the ratio between the total sleeping time and total time in bed. Normally this ratio should be at least 85%. This value can be lowered by for example insomnia (source: American Sleep Apnea Association).

ANT is an efficient low power, wireless protocol that operates in the 2.4GHz ISM RF band. ActiGraph's wGT3X+ accelerometers contain hardware that allow them to communicate with ActiLife, ActiLife Mobile, and other peripheral devices using the ANT protocol. Because of bandwidth limitations, ANT wireless communication with ActiLife is limited to status-type information including battery life; remaining memory; and daily and overall wear time, kcal, and cut point summaries.

API OverviewThe ActiLife API or "Application Programming Interface" is a new feature available in ActiLife which allows programmers to access key ActiLife functionality from within their own custom applications. Currently, the API provides control of USB device initialization and download, wireless initialization, wireless burst downloads, LED identification, and ActiLife application launching, minimizing and restoring. More features are being added daily.

API Use CasesThe API feature is designed for our customers who require specific and consistent responses from our ActiLife software through their own custom web or client-side applications. Examples include:

Pharmaceutical clinical trials in which ActiLife is required to operate within a 21 CFR Part 11 compliant system

Large scale research projects that require consistent data capture from lab technicians who are geographically dispersed

Web applications designed to capture objective actigraphy data from patients (wired or wirelessly)

Enabling the API FeatureThe API feature is currently enabled manually by an ActiGraph customer support representative. Contact ActiGraph for more information, including pricing, at sales@actigraphy.nl

Our Low Frequency Extension (LFE) filter is designed to detect lower amplitude movements over our standard filter. The sensitives of solid state MEMS accelerometers like the one utilized in ActiGraph's products are very high and can detect even the most minute of movements (+/- 6G's for our GT3X+/wGT3X+/ActiSleep+/wActiSleep+ devices and +/-3G's for our legacy products).

Because we are only interested in movements that are attributable to human activity and not minor vibrations found in our everyday environment, we have an internal acceleration threshold that must be crossed before we consider it human movement related and as a result, record it. This threshold limits the ability of the device to measure movement of populations such as the elderly or any population that is very slow and/or exhibits very low acceleration outputs.

In order to combat this, and in order to provide researchers the ability to gather useful information about this subject group we have provided the LFE which reduces this threshold. The net effect is that the reduced amplitude movements are now recognized and recorded, yielding information of value.

Older versions of ActiLife produced *.dat files when downloading data from devices. These files consist of ten(10) lines of header data (meta information about the content) followed by epoch accumulated/filtered data. This“DAT” file stores all of the activity (1 to 3 axis), pedometer, Inclinometer, and/or heart rate data in ASCIIformat. This file also contains the Serial Number, Start Time, Start Date, Epoch Period, Download Time,Download Date, Current Memory Address Pointer, Current Battery Voltage, Mode and First Start Time (usedto build the DAT file) as part of its ten (10) line header information. The file can be viewed with any standard texteditor such as MS Notepad. The Mode value given in the header of the DAT file outlines which features wereactive during data collection for the file.

Note that theActiTrainer records workout data in a separate memory slot. This separation is done primarily for commercialcustomers, runners for example, that are interested in obtaining the exact number of steps taken or distancetravelled as soon as a workout is enabled, which could occur in the middle of an epoch. Workouts can only beenabled while the ActiTrainer display is enabled and only during 60 second epochs.

The data table in the *agd database consist of the following parameters:

TimeStamp – a measure of total Ticks. A single tick represents one hundred nanoseconds or one tenmillionthof a second. There are 10,000 ticks in a millisecond. The value of this property represents the numberof 100-nanosecond intervals that have elapsed since 12:00:00 midnight, January 1, 0001. More informationavailable at Microsoft (http://msdn.microsoft.com/en- us/library/system.datetime.ticks.aspx)

hr (optional) –Heart beats-per-epoch. This data is only gathered by ActiTrainer devices when those devices are initialized tocollect heart rate data AND when the Polar® Heart Monitor (wireless heart strap) is worn by the user.

Lux(optional) – Ambient light sampled once per second (on GT3X+ and ASM devices only) and averaged over thelength of the selected epoch. (e.g., for 60s epochs, this would be the light values summed once per second anddivided by 60).

Inclinometer (optional) – Device orientation information. This is calculated by sampling theangle of the device over the entire length of the epoch (30 times per second) and selecting the predominant angle;i.e, the angle “range” that appeared the majority of the time over the epoch.

The Cole Kripke algorithm was derived from research performed by Roger Cole, and Daniel Kripke in the technical note Automatic Sleep/Wake Identification from Wrist Actigraphy. This paper is also available on ActiGraph's research database here. This algorithm is primarly used to score adult populations.

Users wishing to create their own sleep algorithm based on the techniques used in the Sadeh and Cole-Kripke may create their own algorithm using the "Custom Sleep Score Algorithm Builder".

The Sadeh algorithm was derived from fundamental research performed by Avi Sadeh, Katherine Sharkey, and Mary Carskadon entitled Activity Based Sleep-Wake Identification: An Empirical Test of Methodological Issues. This paper is available on ActiGraph's research database here. This algorithm is primarily used for younger adolescents as most of the research was performed on children and young adults.

The linearity of this equation allows ActiLife to scale non-60 second epochs up to their 60s equivalent then back down again to obtain a per-epoch kcal value.

Freedson VM3 ('11). This equation was derived by Dr. Jeffer Sasaki, Dr. Dinesh John, and Dr. Patty Freedson in the publication Validatoin and Comparison of ActiGraph Activity Monitors published in April of 2011. This equation uses all three axes (if data is collected on all three axes) to estimate energy expenditure. The Vector Magnitude calculation is only valid if the epoch counts exceed the Scale×2453.

The linearity of this equation allows ActiLife to scale non-60 second epochs up to their 60s equivalent then back down again to obtain a per-epoch kcal value.

Freedson VM3 Combination ('11). This option combines the Freedson VM3 ('11) formula with the Williams Work-Energy ('98) equation when the VMCPM (described previously) is less than 2453 counts per minute.

There have been many questions and inquiries as to the origin of the term ‘count’ in relation to activity measurements. Below is an explanation of why ActiGraph uses this unit of measurement and what it represents in the physical world.

"Count" is an enduring term. Prior to solid-state analog-to-digital converter commercial availability, activity monitors utilized either the 'threshold crossing' or 'cycle count' activity measurement. The threshold crossing technique involved incrementing a "count" each time the magnitude of acceleration (activity) exceeded a given threshold. The cycle count approach produced a "count" when enough force was applied to move a mechanical lever through a full cycle (up and down). The latter of these two approaches is very similar in nature to the modern day pedometer measurement technique.

ActiGraph's original activity monitor, the 7164 model, utilized a mechanical lever capable of measuring the change in acceleration with respect to time (g/sec, where g is gravity or 9.806 m/s2). To suppress unwanted motion and enhance human activity, the acceleration signal was passed through an analog band-pass filter, the output of which yields a dynamic range of 4.26g/sec (+/-2.13g/sec) at 0.75Hz (center frequency of the filter). Using a sample rate of 10 samples-per-second, this filtered signal was then digitized into 256 distinct levels by an 8-bit solid-state analog-to-digital converter, producing 4.26g/sec per 256 levels or 0.01664 g/sec/count (each level is considered 1 count). When each filtered sample is multiplied by the sample window of 0.1sec, a resolution of 0.001664g/count is achieved.

The *.agd file format is ActiLife’s native file format and is the desired file type for all ActiLife operations andtools such as Wear Time Validation, Data Scoring, Sleep Scoring, and Graphing. *.agd files are database filesformatted for use with the popular SQLite architecture (www.sqlite.org). These files can be viewed in theActiLife AGD viewer (available from the File menu). Alternatively, the SQLite browser (publically available onthe SQLite website) can be used to browse the details of the *.agd files. AGD File Schema: *.agd files arecreated in ActiLife immediately when data from a GT1M, GT3X, ActiTrainer, or ASM device is downloadedthrough the “Devices” tab. These *.agd files typically contain post-filtered and accumulated data as this is theonly type of data that ActiLife can properly handle. The rows in the database represent whole-number epochsummations (1s, 5s, 10s, 30s, 60s, etc.). However, at the time of this writing all GT1M, GT3X, ActiTrainer, andASM products that have been initialized to collect raw data (12Hz or 30Hz) will produce an *.agd file ondownload. Although the file cannot be processed in ActiLife, the *.agd format can be exported to *.csv or *.datthrough ActiLife’s import/export tool or AGD viewer tool, both accessible from the File menu. GT3X+ devicesproduct a *.gt3x file on download (see *.gt3x File Format). In order to process in ActiLife, the file must beexported to *.agd format. This can be done at the time of download by checking "Create AGD File" from thedownload dialog box or by using ActiLife's built-in import/export tool available from the File menu.

What are the Floating Window Criteria in the Wear Time Validation tool?

The Floating Window Criteria settings are used to define windows of inactivity. These windows of inactivity(also called "non-wear periods") can be flagged and excluded from further analysis within ActiLife. This meansthat you will have the option in the Data Scoring tool to exclude these "non-wear periods" from your analysis.This is useful if you're trying to analyze periods of real activity rather than periods when the subject did not wearthe device. Wear Time Validation is used to:

1. Exclude non-wear activity periods from further data analysis (asexplained above) 2. Simply identify periods when the subject did not meet prescribed activity requirements(i.e., the user was inactive for large amounts of time while wearing the device).

The Floating Window Criteriaparameters are explained below.

- Valid Activity Floor - The minimum count level per minute to qualify a "valid"activity period. Note that this value is scaled to one minute intervals. For example, if the Valid Activity Floor isset to 240 counts per minute, this implies a floor of 40 counts per epoch for a 10 second epoch file. In thisexample, each epoch that exceeds 40 activity counts will contribute 10 seconds of wear-time" toward the weartimesum. Once the sum of the wear-time" exceeds the "Flag After..." length (in minutes), the non-wear periodwill end and a wear-period will begin.

- Use Vector Magnitude - When checked, the Valid Activity Floor appliesto the vector magnitude of the 3-axis data collected by the device. If the device does not have 3-axis dataavailable, only the vertical axis is used. When unchecked, only the vertical axis data is used to calculate weartime validation.

- Flag as "non-wear" after.... - The minimum total of non-wear time required to score a period asa non-wear period. In the previous example (using 10 second epochs), a minimum of 60 consecutive epochs(assuming the default "Flag as "non-wear" after..." time is 10 minutes) must be encountered in order to score aperiod as "non-wear" time. This example does not take into account the Drop Time or tolerance which isexplained below.

- Drop Time (tolerance) - The drop time provides the user with a tolerance window for nonwearperiods and allows the user to take into account (i.e., filter out) small spikes in activity which should not beconsidered valid wear time. In the previous example, setting the drop time to a default of 2 minutes means that anon-wear period may contain up to 12 epochs (not necessarily consecutive) of data greater than 40 counts. Anon-wear period will no longer be scored as a non-wear period when the tolerance is exceeded.

The Wear Time Validation (WTV) tool in ActiLife allows users to easily flag invalid data (or, data collected when a device was not worn) for exclusion from further analysis. WTV also provides a summary of the wear-time and non-wear time results from the datasets. The Data Screening Criteria allow users to define non-wear periods. Data flagged as non-wear is not removed from the *.agd file - it is simply flagged as "wear time" or "non-wear time".

Define a non-wear periodThis section provides options for defining the non-wear period. ActiLife will use this information to scan the data for non-wear bouts.

Minimum Length (minutes or epochs) - This option sets the minimum required number or consecutive zeros (or data below the Activity Threshold if that feature is enabled) that must be encountered before a period is considered a "non-wear period." After scanning the data and detecting the selected number of consecutive zeros, ActiLife will begin flagging data as "non-wear time." The leading zeros detected during this process will be flagged retroactively (after they have been initially scanned). Data will be flagged as "non-wear time" until the Spike Tolerance is exceeded. That is, until the non-wear bout is broken by activity that exceeds 0 (or the Activity Threshold) for the number of times defined by Spike Tolerance.

Activity Threshold (per minute or per epoch) - This value represents the non-wear "floor." When this feature is disabled, a non-wear bout is identified as periods of zeros (0s) in the data. When enabled, any epoch below the set value will be considered as "zero." Any epoch level over this threshold is considered a Spike

Max count level (per epoch or per minute) - Setting this option will flag epoch (or minute) counts that exceed this threshold as "zeros". This option is useful if the dataset contains some corrupt data, for example, with large count levels. This option can be used to exclude those count levels from wear-time analysis.

Spike Tolerance (number of minutes or epochs) - ActiLife will continue scoring a non-wear bout as non-wear until it detects more than the Spike Tolerance number of epochs above zero (or above theActivity Threshold, if that option is enabled).

Spike level to stop (count level per epoch or per minute) - If this option is set, a non-wear bout will end if a count level that exceeds this value is encountered. This option allows the Wear Time Validation tool to mimic exactly the behavior of the 2003-2004 NHANES SAS code which eliminates non-wear time using the same technique.

Optional Screening Parameters

Ignore wear periods less than _____ (minutes or epochs) - Because of the WTV algorithms used, some wear periods may be very short (shorter than the spike tolerance). Use this option to set the minimum length of an acceptable wear period.

Minimum wear time per day (minutes or epochs) - Use this setting to flag an entire 24 hour day as invalid if there are less than the selected amount of valid (wear-time) minutes or epochs in the day.

Minimum days of valid wear time - Use this option to flag an entire dataset as invalid if there are less than the minimum number of valid days.

ActiLife is not supported in a Virtual Environment like VMware, VirtualBox, or Parallels. It is also not supported in a Terminal Services environment and does not work with Roaming Profiles or profile folder redirection.

The Wear Time Validation tool in ActiLife allows users to screen epoch-level *.agd files (collected from any ActiGraph device) to flag periods of non-wear. Non-wear is estimated by analyzing periods of little or no activity and applying algorithms to those periods to determine if the user was actually wearing the device or not.

Although ActiLife offers two Wear Time Validation algorithms, the "Daily" algorithm is deprecated - users are advised not to use this algorithm.

This article aims to describe the difference between the "Daily" and the "Floating Window" wear time validation algorithm.

CommonalitiesBoth algorithms have the following features in common.

Detects wear-time vs. non-wear time by examining the number and behavior of "zeros" in the data

Flags, does not modify, epochs within the *.agd file as "wear" or "non-wear"

Gives the ability to characterize a "zero" as being higher than "zero" (activity floor) or above a certain high threshold (activity ceiling)

Flags an entire day within a dataset as invalid if minimum "valid hours" requirement are not met

Flags an entire dataset as invalid if minimum "valid days" criteria are not met

"Daily" Wear Time Validation

The "Daily" option breaks a file down into calendar hours and calendar days. Essentially, an entire HOUR will be flagged as valid or invalid based on the validation criteria. At the end of each hour, the analysis starts over.

"Floating Window" Wear Time Validation

The "Floating Window" algorithm does not break a file into hours but rather looks at consecutive epochs for patterns. Users can choose, for instance, to say "only consider 75 minutes of consecutive zeros as non-wear". After that pattern is detected, all data within that range and the data that follow will be flagged as non-wear. There are no calendar hour/day constraints to detecting non-wear time. This algorithm provides the user with much more flexibility by allowing the user to set tolerance levels, minimum wear-time lengths, and non-wear stop levels. For a full description of the options available in this algorithm, see Wear Time Validation Parameters.

GT3X+ devices produce an interim compressed file with file extension *.gt3x. This is a compressed file and mustbe extracted to a usable format using ActiLife’s import/export tool which is accessible from the File menu inActiLife. The file can be exported to *.agd format for processing in ActiLife or *.csv/*.dat format for processingusing third party tools. The *.gt3x file can also be exported directly to *.agd format during the download processby checking “Create AGD File” from the download prompt.

ActiLife offers 12 different MET algorithms to determine the average hourly and daily and per-epoch metabolic rate (link to Wikipedia article on Metabolic Rate) for datasets. In short, 1 MET represents the amount of energy the human body expends at rest. This is equivalent to their Basal Metabolic Rate (or BMR). A MET rate of 2.0, for instance, indicates that during that time period, the subject was expending twice their normal sedentary energy (BMR*2). This value will never fall below 1.0.

All of the MET formulas used in ActiLife and listed below identify the MET rate using Counts Per Minute (or CPMs - see "What are counts?" for more info). Files that contain sub-minute data (e.g., 10s epoch data) are scaled up (multiplied by a factor to obtain the 60 second equivalent).

Special notes: Note that data was collected from adults walking on a treadmill. According to Freedson et., al., "These data suggest that 1258 counts corresponds to a 1 MET change between 3 and 9 METs using 1951 counts as the 'baseline' to define the 3 MET level" which implies this formula may not produce valid results below 3 METs or above 9 METs. For the sake of usability in ActiLife, the forumula will provide data when the CPM>=100. However, no guarantees can be made about this extrapolation. Contact the author for further information.

Finally, once a MET value has been calculated for each 10 sec epoch within a minute on the ActiGraph clock, the average MET value of 6 consecutive 10-sec epochs within each minute is calculated to obtain the average MET value for that minute

Methods: Forty-eight participants [mean (sd) age 35 yrs (11.4)] performed 10-min bouts of various activities ranging from sedentary behaviors to vigorous physical activity. Eighteen activities were divided into three routines and 20 participants performed each routine. Participants wore an ActiGraph accelerometer on the hip and a portable indirect calorimeter was used to measure energy expenditure. Forty-five routines were used to develop the refined 2-regression model and 15 routines were used to cross-validate the model. Coefficient of variation (CV) was used to classify each activity as continuous walking/running (CV?10) or intermittent lifestyle activity (CV>10).

Methods: Twenty-five subjects completed four bouts of overground walking at a range of self-selected speeds, played two holes of golf, and performed indoor (window washing, dusting, vacuuming) and outdoor (lawn mowing, planting shrubs) household tasks. Energy expenditure was measured using a portable metabolic system, and motion was recorded using a Yamax Digiwalker pedometer (walking only), a Computer Science and Application, Inc. (CSA) accelerometer, and a Tritrac accelerometer. Correlations between accelerometer counts and energy cost were examined. In addition, individual equations to predict METs from counts were developed from the walking data and applied to the other activities to compare the relationships between counts and energy cost.

Methods: Seventy participants completed one to six activities within the categories of yard work, housework, family care, occupation, recreation, and conditioning, for a total of S to 12 participants tested per activity. EE was measured using the Cosmed K4b12210 portable metabolic system. Simultaneously, two Computer Science and Applications, Inc. (CSA) accelerometers (model 7164), one worn on the wrist arid one worn on the hip, recorded body movement. Correlations between EE measured by the Cosmed and the counts recorded by the CSA accelerometers were calculated, and regression equations were developed to predict EE from the GSA data.

Methods: In a laboratory study, 28 subjects (14 men, 14 women) walked at a normal pace, walked at a fast pace, and jogged at a comfortable pace on an indoor track. These activities were repeated on a treadmill using the individual speeds from the track locomotion. Oxygen uptake was measured simultaneously using a portable metabolic system. One activity monitor was worn on the hip and one on the lower back. In a field study, 34 subjects (18 men, 16 women) each wore two monitors (hip and low back placement) for seven consecutive days.

Methods: Seventy-two 35- to 45-yr-old volunteers walked around a level, paved quadrangle at what they perceived to be a moderate pace. Oxygen consumption was measured using the criterion Douglas bag technique. Speed, CSA(hip), heart rate, and Borg rating of perceived exertion were also monitored.

ActiLife 6 is ActiGraph's premier actigraphy data analysis and management platform. Trusted by researchers and healthcare providers around the world, ActiLife 6 is used to prepare ActiGraph devices for data collection and to download, process, score and securely manage collected data. ActiLife 6 has an extensive selection of integrated customer-driven features and analysis tools designed to help our clients achieve a broad range of research and clinical objectives.